class_docstr: Rank cross entropy loss.
  
  Examples:
      >>> from keras import backend as K
      >>> softmax = lambda x: np.exp(x)/np.sum(np.exp(x), axis=0)
      >>> x_pred = K.variable(np.array([[1.0], [1.2], [0.8]]))
      >>> x_true = K.variable(np.array([[1], [0], [0]]))
      >>> expect = -np.log(softmax(np.array([[1.0], [1.2], [0.8]])))
      >>> loss = K.eval(RankCrossEntropyLoss(num_neg=2)(x_true, x_pred))
      >>> np.isclose(loss, expect[0]).all()
      True
docstr: `num_neg` getter.
docstr_tok: ["`","num_neg","`","getter","."]
code: def num_neg(self):
      """`num_neg` getter."""
      return self._num_neg
  
file_path: MatchZoo/matchzoo/losses/rank_cross_entropy_loss.py
imports: import numpy
  import tensorflow
  from keras import layers, backend
  from keras.losses import Loss
  from keras.utils import losses_utils
label_desc: Use image node_id 3 for calling the RankCrossEntropyLoss obj's underlying member method code with example usage: obj.num_neg() and returns: self